This invention relates generally to social networking, and in particular to inferring household income for users of a social networking system for targeting advertisements.
Traditional targeting criteria for advertising relies on demographic data and structured information, such as a user's self-declared interests and intentions to be marketable (e.g., to be in the market to purchase a product or service). Advertisers, in an effort to locate and target these users, purchase analytical data gathered by third parties that track users visiting websites related to the advertiser's product. For example, websites on the Internet track people comparing car prices and filling out a form for a test drive at a local dealership and sell this information to advertisers. Ultimately, advertisers depend on this analytical data, which assumes users have enough household income to make a purchase.
In recent years, users of social networking systems have shared their interests and engaged with other users of the social networking systems by sharing photos, real-time status updates, and playing social games. The amount of information gathered from users is staggering—information describing recent moves to a new city, graduations, births, engagements, marriages, and the like. Social networking systems have been passively recording this information as part of the user experience, but social networking systems have lacked tools to synthesize this information about users for targeting advertisements based on their perceived income.
Specifically, the information available on social networking systems has not been used to infer the household income of users of a social networking system. Information about users' household income is very valuable to advertisers that seek to market luxury goods and services to these users. However, existing systems have not provided advertisers with users that have been categorized by inferred household income ranges.